Apparatuses and methods for anomalous gas concentration detection

ABSTRACT

Embodiments of the disclosure are drawn to apparatuses and methods for anomalous gas concentration detection. A spectroscopic system, such as a wavelength modulated spectroscopy (WMS) system may measure gas concentrations in a target area. However, noise, such as speckle noise, may interfere with measuring relatively low concentrations of gas, and may lead to false positives. A noise model, which includes a contribution from a speckle noise model, may be used to process data from the spectroscopic system. An adaptive threshold may be applied based on an expected amount of noise. A speckle filter may remove measurements which are outliers based on a measurement of their noise. Plume detection may be used to determine a presence of gas plumes. Each of these processing steps may be associated with a confidence, which may be used to determine an overall confidence in the processed measurements/gas plumes.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a 35 U.S.C. § 371 National Stage Application of PCTApplication No. PCT/US2018/061120, filed Nov. 14, 2018, which claims thebenefit under 35 U.S.C. § 119 of the earlier filing date of U.S.Provisional Application No. 62/586,008, filed Nov. 14, 2017, the entirecontents of which are hereby incorporated by reference, in theirentirety, for any purpose.

BACKGROUND

Sensors for measuring and monitoring gas concentrations over large areasare important tools for wide variety of traditional and emergingapplications. Many sensor technologies have been deployed for large-areagas concentration measurements and monitoring. Examples include activeremote sensing techniques, such as certain forms of light detection andranging (lidar) and open-path spectroscopy systems, as well as passiveremote sensing techniques including imaging spectrometers and opticalgas cameras. In addition to remote sensing techniques, distributed pointsensor networks and mobile point sensors have been deployed, which mayrequire gas intake for measurements.

Several performance tradeoffs exist between the various types of remotesensors. For instance, passive remote sensors may enable highmeasurement rates, and therefore may be used to more rapidly cover largeareas. However, passive sensors may exhibit low detection reliability,higher false positive rates, and poorer sensitivity compared to theiractive remote sensor counterparts. For example, state-of-the-artairborne optical gas cameras typically quote methane detectionsensitivities in the thousands of ppm-m, and are highly dependent onambient conditions. Shadows, clouds, and varying background reflectivityfrom one object or portion of a scene to the next can confound passiveremote sensors and make reliable, sensitive detection challenging.Passive sensors may therefore be best suited for detection of thelargest leaks. The relatively poor sensitivity of passive measurementsmay also result in an unacceptably high probability of misseddetections—in some cases of relatively large leaks. In contrast, lidartechniques such as wavelength modulation spectroscopy (WMS),differential absorption lidar (DIAL) and tunable diode laser absorptionspectroscopy (TDLAS) may achieve methane detection concentrationsensitivities of tens of ppm-m or less, which may enable detection ofmuch smaller leaks and during windy, cloudy, or varying backgroundconditions.

In addition to detection sensitivity, lidar sensors may benefit fromhigh spectral selectivity of targeted gas species compared to passivesensors. These properties of lidar measurements may result from therelative consistency of active laser illumination of remote targets andselective detection schemes used to process light signals received bylidar sensors. Selectivity of the target gas species may make lidarsensors especially well-suited for quantification of regions ofanomalous gas concentration. Specifically, leak rate quantification ofdetected plumes may be desirable because it may allow classification andprioritization of detected leaks.

SUMMARY

In at least one aspect, the present disclosure may relate to a methodwhich may include obtaining, using a light detection and ranging (LIDAR)system, a set of gas concentration measurements from a target area. Themethod may include discarding or modifying certain measurements of theset of gas concentration measurements based on a comparison ofmeasurements in the set of gas concentration measurements to an adaptivethreshold with a value based on an expected noise level. The value ofthe adaptive threshold may vary depending on parameters of themeasurement. The method may include determining a presence of ananomalous gas concentration based on the revised set of measurements.

The method may also include determining a confidence that a remainder ofthe set of gas concentration measurements after discarding or modifyingthe certain measurements represent anomalous gas concentrations. Theexpected noise level may be based, at least in part, on a noise modelcomprising a model of speckle noise in the set of gas concentrationmeasurements. The noise model may also include a detector noise model.The value of the adaptive threshold may be a multiple of the expectednoise level. The value of the adaptive threshold may be used todetermine a confidence that gas concentration measurements which areabove the value represent true positives (e.g., as opposed to falsepositives). The value of the adaptive threshold may be based, at leastin part, on an amount of light received by the LIDAR system.

In at least one aspect, the present disclosure may relate to a methodwhich may include obtaining, using a light detection and ranging (LIDAR)system including a laser source modulated at a modulation frequency, aset of gas concentration measurements from a target area. The method mayinclude discarding or modifying certain measurements of the set of gasconcentration measurements based at least in part on a signal amplitudepresent in at least one odd harmonic of the modulation frequency toprovide a revised set of measurements. The method may includedetermining a presence of an anomalous gas concentration based on therevised set of measurements.

The method may also include measuring an amount of speckle noise in themeasurement based on the signal amplitude. The method may also includedetermining an expected amount of speckle noise based on a speckle noisemodel, and comparing the measured amount of speckle noise to theexpected amount of speckle noise. The method may also includedetermining a confidence that a remainder of the set of gasconcentration measurements after discarding or modifying the certainmeasurements represent anomalous gas concentrations.

In at least one aspect, the present disclosure may relate to a methodwhich may include obtaining, using a light detection and ranging (LIDAR)system, a set of gas concentration measurements from a target area. Themethod may include determining, based on a speckle noise model, at leastone anomalous gas concentration measurement in the set of gasconcentration measurements. The method may include determining apresence of a gas plume associated with the at least one anomalous gasconcentration measurement and one or more of the set of gasconcentration measurements nearby a location of the at least oneanomalous gas concentration measurement.

The method may also include determining a direction, location and/orsource of the gas plume. The method may also include determining if eachof the at least one anomalous gas concentration measurements isassociated with a gas plume, and modifying or discarding certain of theat least one anomalous gas concentration measurements which are notassociated with a gas plume. The determining the presence of the gasplume may include integrating along a plurality of lines which areperpendicular to the direction of the gas plume.

In at least one aspect, the present disclosure may relate to anapparatus which may include an optical system, at least one processor,and a memory. The optical system may include a laser source which may bemodulated at a modulation frequency. The optical system may record a setof gas concentration measurements based on received light from a targetarea. The memory may be encoded with executable instructions, which maybe executed by the at least one processor. The executable instructionsmay cause the apparatus to discard or modify certain measurements of theset of gas concentration measurements based on a comparison ofmeasurements in the set of gas concentration measurements to an adaptivethreshold to provide a first revised set of measurements. The adaptivethreshold may have a value based on an expected noise level. Theexecutable instructions may cause the apparatus to identify certain ofthe measurements of the first revised set of measurements as outliersand discard or modify the identified outliers to provide a secondrevised set of measurements. The executable instructions may cause theapparatus to determine a presence of a gas plume based on at least onemeasurement point in the second revised set of measurements and discardor modify measurements of the second revised set of measurements whichare not associated with the gas plume.

The executable instructions may also include instructions to cause theapparatus to determine a detection confidence. The executableinstructions may also include instructions to cause the apparatus todetermine a first confidence based on the first revised set ofmeasurements, a second confidence based on the second revised set ofmeasurements, and a third confidence based on the gas plume, and whereinthe processor may determine the detection confidence based on the first,second, and third confidences. The executable instructions may alsoinclude instructions to cause the apparatus to generate a map based onthe detection confidence.

The apparatus may also include a mobile platform which may support theoptical system and move relative to the target area. The optical systemmay determine range information between the optical system and surfacesof the target area, and wherein the map is based on the gas plume andthe range information.

The executable instructions may also include instructions to cause theapparatus to determine the expected noise level based on a noise modelcomprising a speckle noise model. The executable instructions may alsoinclude instructions to cause the apparatus to identify the outliersbased, at least in part, on a signal amplitude present in at least oneodd harmonic of the modulation frequency. The executable instructionsmay also include instructions to cause the apparatus to determine thepresence of the gas plume based on an angular dependence of theconcentration about the at least one measurement point in the second setof revised measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a measurement system according to anembodiment of the present disclosure.

FIG. 2 is a block diagram of a computing system according to anembodiment of the present disclosure.

FIG. 3 is an example image of plume detection according to an embodimentof the present disclosure.

FIG. 4 is a graph depicting a detection limit according to an embodimentof the present disclosure.

FIG. 5 is a graph of in-phase and out-of-phase harmonics according to anembodiment of the present disclosure.

FIG. 6 is a schematic diagram depicting gas plume detection according toan embodiment of the present disclosure.

FIGS. 7A-7B are graphs depicting using plume detection as a filteraccording to an embodiment of the present disclosure.

FIG. 8 is a graph depicting multiple filters applied to spectroscopydata according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The following description of certain embodiments is merely exemplary innature and is in no way intended to limit the scope of the disclosure orits applications or uses. In the following detailed description ofembodiments of the present systems and methods, reference is made to theaccompanying drawings which form a part hereof, and which are shown byway of illustration specific embodiments in which the described systemsand methods may be practiced. These embodiments are described insufficient detail to enable those skilled in the art to practicepresently disclosed systems and methods, and it is to be understood thatother embodiments may be utilized and that structural and logicalchanges may be made without departing from the spirit and scope of thedisclosure. Moreover, for the purpose of clarity, detailed descriptionsof certain features will not be discussed when they would be apparent tothose with skill in the art so as not to obscure the description ofembodiments of the disclosure. The following detailed description istherefore not to be taken in a limiting sense, and the scope of thedisclosure is defined only by the appended claims.

Spectroscopy may be used in a wide array of applications to determineproperties of a target based on the interaction of different wavelengthsof electromagnetic radiation with the target. An optical system maydirect light from a transmitter (e.g., a light source, a telescope,etc.) onto the target, and/or may direct light from the target (e.g.,reflected and/or scattered light) onto a receiver (e.g., a camera, atelescope, etc.). Measurements of the received light incident on thereceiver may be used to determine one or more properties of the target.In an example application, the target may be a gas, and a concentrationof the gas may be calculated based on a measurement of the lightreceived, compared to the light transmitted, or based on any othermethod. In some embodiments, wavelength modulation spectroscopy may beused, where the concentration of the gas may be calculated based on ameasurement of the light received at a given wavelength at a givenwavelength modulation frequency compared to the light transmitted atthat wavelength.

Spectroscopy may be used to determine if the concentration of aparticular gas is anomalous. An anomalous concentration may represent aspatial region where the concentration of a given gas is greater thansome background concentration of the gas. For example, the anomalousconcentration may represent a leak of an industrial gas (e.g., methane)into a surrounding environment. Detection and/or location of anomalousconcentrations of certain gases may be used to determine one or moreactions, such as remediating a leak or other problem, monitoringenvironmental conditions, evacuating an area, or others.

In some scenarios, it may be important to be able to detect relativelylow concentrations of the gas. However, spectroscopy measurements mayinclude a contribution from noise. The noise may be due, for example, tophysical properties of the system (e.g., detector noise, thermal noise,etc.) and/or properties of the light (e.g., speckle noise). In somecases, the amount of noise may be similar to the level of the measuredsignals associated with the anomalous gas concentrations. The noise maylead to false positives, where a relatively high amount of noise in agiven measurement causes the system to treat that measurement as ananomalous gas concentration, even though there is not one. Sincedetected anomalous gas concentrations may lead to expensive and/ortime-consuming actions (e.g., shutting down a pipe believed to be leaky,or deploying repair crews), it may be desirable to minimize the numberof false positives. However, it may also be important to detectrelatively low concentrations of the gas (e.g., to have a low limit ofdetection) so that, for example, even relatively small and/or slow leakscan be detected (e.g., to reduce false negatives). Thus, it may beimportant to process a set of spectroscopy measurements to determinewhich measurements represent an anomalous concentration of gas, andwhich are due to noise.

The present disclosure provides examples of apparatuses and methods fordetecting anomalous gas concentrations. After spectroscopy measurementsare collected, they may be processed to eliminate (and/or reduce) falsepositives. A noise model may be used which includes a speckle noisemodel. For each of the spectroscopy measurements (e.g., for each pointof the measurements) the noise model may be used to calculate anexpected amount of noise, and an adaptive threshold may be generatedbased on the expected amount of noise. The adaptive threshold may be setbased on the expected amount of noise in individual, separate, grouped,averaged, or any other combination or processed measurement values. Insome embodiments, the adaptive threshold may be set based on theexpected amount of noise in a given set of measurement data. Theadaptive threshold may be used to filter the measurements. The amount orseverity of speckle noise in individual or groups of the measurementsmay also be measured and used to determine if a given measurement is anoutlier or not. The adaptive threshold and the amount of speckle noisemay be used (alone or together) to filter each of the spectroscopymeasurements (e.g., by removing certain points, applying a weight tocertain points, etc.).

Additionally, plume detection may be used to further filter the data,since measurement points with anomalous gas concentrations are likely tohave neighboring or nearby points with elevated gas concentration. Theplume detection may be used on its own, or may be used with the adaptivethresholding and/or speckle measurement. Each of these processing stepsmay include a calculated confidence, which may represent a probabilitythat a detected anomalous gas concentration is a true positive. Theconfidence and/or concentrations may be plotted to form a map or otherspatial distribution. In some embodiments, the computed confidence maybe used to label one or more plumes on a map showing gas concentration.

FIG. 1 is a block diagram of a measurement system according to anembodiment of the present disclosure. The measurement system 100includes an optical system 102 and a computing system 104. The opticalsystem 102 includes a transmitter 106, which provides emitted light to ascanner 108, which directs an example light ray 110 towards a targetarea 116. The target area 116 may include a gas source 118 which emits agas 120. The light ray 110 may interact with the gas 120, and a portionof the light may return to the optical system 102 and be measured by areceiver 112. The computing system 104 includes one or more componentssuch as a controller 122, a communications module 124, a processor 126,and/or a memory 128. All or part of the measurement system 100 may bemounted on a mobile platform 114, which may have a direction of motion130 relative to a target area 116.

In some embodiments, the measurement system 100 may be a light detectionand ranging (lidar) system. The lidar system may use lasers to detectgas 120, as well as optionally performing one or more other measurements(e.g., distance). In some embodiments, the measurement system 100 may bea spectroscopic system (e.g., wavelength modulation spectroscopy) andone or more properties of the gas 120 (e.g., type, composition,concentration, etc.) may be determined based, at least in part, onspectroscopic measurements. In some embodiments, the measurement system100 may use wavelength modulation spectroscopy (WMS), where a laser usedto illuminate the target area 116 is modulated.

The measurement system 100 may take a plurality of spectroscopicmeasurements, which may be distributed across the target area 116. Insome embodiments, the measurement system 100 may be fixed relative tothe target area 116. In some embodiments, the measurement system 100 maybe mounted on a mobile platform 114, which may move relative to thetarget area 116. In some embodiments, the measurement system 100 mayscan the beam 110 (and/or the field of view of the receiver 112) acrossthe target area 116.

The information gathered by the measurement system 100 may be used todetermine one or more properties of the gas 120 such as a concentrationof the gas 120. The gas 120 may be an anomalous gas, which may normallybe absent from the environment of target area 116 (or may normally be atlow or trace amounts in the environment of the target area 116). In someembodiments the gas 120 may be an environmental hazard, such as methane.In some embodiments, the target area 116 may include a wellsite, apipeline, a pipeline right-of-way, a landfill, a waste water facility, afeedlot, an industrial site, a waste disposal site, or combinationsthereof. The measurement system 100 may generate, as an output, aspatial distribution (e.g., a map) of the concentration of the gas 120.The spatial distribution of concentrations of the gas 120 about thetarget area 116 may be used, for example, to locate a source 118 (e.g.,a leak), and/or determine a flow rate of the gas 120. In someembodiments, one or more actions may be taken based on the measurementsand/or spatial distribution such as, for example, evacuating an area,measuring an environmental hazard, locating a gas leak (e.g.,dispatching one or more personnel to a site indicated by themeasurements and/or spatial distribution), determining a possiblerepair, conducting a repair (e.g. at a location indicated by themeasurements and/or spatial distribution), ensuring regulatorycompliance, or combinations thereof. Other actions may be taken in otherembodiments.

The optical system 102 may provide scanning light and may receivereceived light from the target area 116. The scanning light may berepresented by the light ray 110. The optical system 102 may direct thelight ray 110 along a scan path. The transmitter 106 may provideincident light (e.g., transmitted light), which may interact with (e.g.,be redirected by) the scanner 108 to provide the scanning light. Thescanner 108 may redirect the emitted light towards the target area 116to become the light ray 110. The scanner 108 may change the angle and/ordirection of the light ray 110 over time. In the example embodiment ofFIG. 1, the scanner 108 is shown as a rotating angled reflector,however, any scanner may be used. While a scanner 108 is shown in FIG.1, it should be understood that in some embodiments, the scanner 108 maynot be used. In some embodiments, additional components (e.g., lenses,filters, beam splitters, prisms, refractive gratings, etc.) may beprovided in the measurement system 100 to redirect and/or change otherproperties of the light.

The optical system 102 includes a transmitter 106, which may producetransmitted light. A portion of the transmitted light (which, in someembodiments may be substantially all of the transmitted light) may reachthe scanner 108 as incident light. In some embodiments, the transmitter106 may produce a broad spectrum of light across a range of wavelengths.In some embodiments, the transmitter 106 may produce the transmittedlight with a particular spectrum (e.g., a narrow bandwidth centered on aselected wavelength). In some embodiments, the transmitter 106 mayinclude a laser, and the transmitted light may generally be coherent. Insome embodiments, the controller 122 may cause the spectrum of thetransmitted light to change over time. In some embodiments, thewavelength of the transmitted light may be modulated for WMS. In someembodiments, the wavelength of the transmitted light may be modulatedfor frequency-modulated, continuous-wave (FMCW) LiDAR.

The optical system 102 may also receive light from the target area 116.The received light may be thought of as a bundle of light rays (e.g.,light ray 110) which reach the receiver 112. In some embodiments, thereceived light may be redirected by the scanner 108 onto the receiver112. The size of the area from which light rays reach the receiver 112,and the amount of light which reaches the receiver 112, may be dependenton the field of view of the scanning system 100. In some embodiments,the transmitter 106 and the receiver 112 may be packaged together into asingle unit. In some embodiments, the transmitter 106 and the receiver112 may be coaxial with each other. In some embodiments, a singletransceiver may be used as both the transmitter 106 and the receiver 112(e.g. monostatic transceiver).

The optical system 102 may optionally be mounted on (e.g., supported by)a mobile platform 114, which may move along a direction of motion 130relative to the target area 116. In some embodiments, the mobileplatform 114 may be an aerial vehicle. The mobile platform may be manned(e.g., an airplane, a helicopter) or unmanned (e.g., a drone). In someembodiments, the unmanned vehicle may operate based on remoteinstructions from a ground station and/or may operate based on internallogic (e.g., on autopilot).

The motion of the optical system 102 along the direction of motion 128along with the changing angle of the light ray 110 (and area ‘seen’ bythe receiver 116) due to the scanner 108 may cause the light ray 110follow a scan path. The scan path may be generally have a repeatingshape (e.g., a helical shape). In some embodiments, without thedirection of motion 130 of the mobile platform 114, the light ray 110may follow a closed path, such as a circle or an ellipse. In theseembodiments, the motion of the mobile platform 114 may extend the closedpath into the scan path.

The light ray 110 may interact with one or more targets, such as gas120, within the target area 116. In some embodiments, the gas 120 mayredirect (e.g., by scattering, reflection, etc.) a portion of the lightray 110 back along an optical path leading to the receiver 112. In someembodiments, the light ray 110 may interact with the gas 120 (e.g., viaabsorption or dispersion) and then be redirected along an optical pathback towards the receiver 112 by one or more other features of thetarget area 116 (e.g., the ground). In some embodiments, the gas 120 mayboth redirect the light ray 110 and also modify the scanning light(e.g., may absorb, scatter, transmit, and/or reflect the light ray 110).

A portion of the light ray 110 may return to the receiver 112 asreceived light after interacting with the gas 120. The receiver 112 mayinclude one or more detectors, which may generate a measurement (e.g.,of an intensity, wavelength, phase, and/or other property of the light)based on the received light. The measurements may be provided to thecomputing system 104. The computing system 104 may generate a gasconcentration measurement based on the signal from the receiver 112. Asthe light ray 110 scans across the target area 116, multiple gasconcentration measurements may be generated, which may be spatiallydistributed across the target area 116. Certain of the measurements maybe associated with a region including the gas 120, while othermeasurements are associated with regions which do not contain the gas120.

The computing system 104 may determine a presence, location,concentration, flow rate and/or other properties of the gas 120 based onthe measurements. The computing system 104 may use one or more aspects(e.g., wavelength, intensity) of the received light to determine one ormore properties (e.g., concentration, content, etc.) of the gas 120. Insome embodiments, computing system 104 may compare one or more aspectsof the emitted light provided by the transmitter 106 to correspondingaspects of the received light. In some embodiments, computing system 104may direct the controller 122 to modulate the wavelength of the emittedlight provided by the transmitter 106, and computing system 104 maydetermine properties of the gas 120 based on wavelength modulationspectroscopy. The computing system may store one or more pieces ofinformation (e.g., measurements, calculated properties, etc.) in thememory 128 and may send and/or receive information with thecommunications module 124.

The measurement system 100 may determine regions of the target area 116with anomalous concentrations of the gas 120. The anomalousconcentrations of the gas 120 may represent one or more regions wherethere is a concentration of the gas 120 which is greater than abackground level of the gas 120. The measurement system 100 may use adetection threshold, above which a concentration is judged to beanomalous. Noise in the measurement system 100 may be translated into anequivalent concentration of the gas 120. This noise may cause falsepositives, where certain gas concentration measurements are judged to beanomalous even if they are not associated with elevated concentrationsof the gas 120. One source of noise may be speckle noise, which iscaused by interference of coherent light (e.g., laser light). Thespeckle noise may, at least in part, determine a limit of detection ofthe measurement system 100.

The computing system 104 may process measurements from the opticalsystem 102. The computing system 104 may apply one or more of a seriesof processing steps to narrow down the measurements to those which aretrue positives (e.g., by filtering out noise). The computing system 104may include a noise model, which may be used to determine expectedamounts of noise based on measurement conditions, which may include theamount of light received by the receiver 112. The noise model may, inturn, be at least partially based on a speckle noise model, which mayrepresent the amount of expected speckle noise for a given set ofmeasurement conditions. The noise model may also be at least partiallybased on a detector noise model. The computing system 104 may use one ormore processing steps such as adaptive thresholding, speckle filtering,and/or plume detection, each of which may be based, at least in part, ona noise model including a speckle noise model.

The computing system 104 may store one or more executable instructions,and one or more additional pieces of information (e.g., the noise model)in the memory 128. The processor 126 may use the information in thememory 128 along with measurements from the optical system 102 todetermine properties of the gas 120. The processor 126 may operate thecontroller 122 to control the measurement system 100 (e.g., by operatingthe transmitter 106). The computing system 104 may be in communicationwith one or more remote locations via the communications module 124.

In some embodiments, the processor 126 may determine a spatialdistribution of the concentration of the target gas 120. Theconcentration of the gas 120 may be determined based on individualmeasurements which may be swept along the scan path. The processor 126may measure a spatial location of a given measurement (e.g., based onmapping of the target area 116) and/or may determine the spatiallocation based on known location parameters (e.g., based on knownproperties of the direction of motion 130 and/or scan path of the lightbeam 110). In some embodiments, the measurement system 100 may include alocation determination system (e.g., a GPS, an inertial navigationsystem, a range-finding system, etc.) to aid in determining the spatialdistribution. The individual measurements may then be combined with thespatial information to generate the spatial distribution. The spatialinformation may be 2D and/or 3D. While a single processor 126 and memory128 are shown in FIG. 1, in other examples multiple processor(s) and/ormemories may be used—e.g., the processing and storage described hereinmay be distributed in some examples.

The measurements and/or information derived from the measurements (e.g.,a spatial distribution of the measurement) along with other information(e.g., an altitude of the mobile platform 114, a rate of movement of thescanner 108, etc.) may be provided to the memory 128 and/orcommunications module 124. The memory 128 may be used to recordinformation and/or store instructions which may be executed by theprocessor 126 and/or controller 122 to perform the measurements. Thecommunications module 124 may be a wireless communication module (e.g.,radio, Bluetooth, Wi-Fi, etc.) which may be used to transmit informationto one or more remote stations and/or to receive instructions from theremote stations.

In some embodiments, where a mobile platform 114 is used, one or morecomponents of the measurement system 100 may be located off of themobile platform 114. For example, components of the computing system 104such as the memory 128 and/or the processor 126 may be located at aremote station (e.g., a ground station) and may receiveinformation/instructions from and/or provide information/instructions tothe optical system 102 via the communications module 124. Differentarrangements or parts of the measurement system 100 between the mobileplatform 114 and one or more remote stations are possible in otherexamples. Although not shown in FIG. 1, in some embodiments one or moreadditional components may be provided in the measurement system 100(either in the mobile platform 114 or at a remote locationcommunicatively coupled to the other components) such as a userinterface, display, etc.

FIG. 2 is a block diagram of a computing system according to anembodiment of the present disclosure. In some embodiments, the computingsystem 200 may be used to implement the computing system 104 of FIG. 1.The computing system 200 includes one or more processors 206, acontroller 208, a communications module 210 and a locator 212 allcoupled to a memory 214. The memory 214 includes instructions 216 whichmay include particular sets of instructions such as block 218 whichincludes instructions for adaptive thresholding, block 220 whichincludes instructions for speckle rejection; block 222 which includesinstructions for plume identification, and block 224 which includesinstructions for spatial mapping. The memory 214 may include one or moreother components which may be accessed by one or more of theinstructions 216, such as a noise model 226, location information 228,and/or additional measurements 230. The computing system 200 may becoupled to additional components such as a display 202 and aninput/output (I/O) device 204 (e.g., keyboard, mouse, touchscreen,etc.).

While certain blocks and components are shown in the example computingsystem 200, it should be understood that different arrangements withmore, less, or different components may be used in other embodiments ofthe present disclosure. For example, while a single processor block 206is shown in the computing system 200, multiple processors may be used.In some embodiments, different processors may be associated withdifferent processes of the computing system 200, such as with differentinstructions 216 in the memory 214, or with different functions (e.g., agraphics processor). While the example computing system 200 is shown asa single block, it should be understood that the computing system 200may be spread across multiple computers. For example, a first computermay be located near the optical system (e.g., a computer on mobileplatform 114 of FIG. 1), while a second computer may be at a remotelocation. The various components of a computing system 200 may becoupled by any combination of wired and/or wireless connections (e.g.,cables, wires, Wi-Fi, Bluetooth, etc.).

The processor 206 may access the memory 214 to execute one or moreinstructions 216. Based on the instructions 216, the processor 206 mayprocess measurements from an optical system (e.g., optical system 102 ofFIG. 1). The processor 206 may receive measurements “live” from theoptical system as the measurements are generated (e.g., measurements maybe streamed, provided real-time, or otherwise dynamically transferred),and/or may retrieve measurements 230 which were previously stored in thememory 214. In some examples, the instructions 216 may cause theprocessor 206 to process the measurements by filtering the measurements,adjusting the measurements, generating new data based on themeasurements, and/or storing the measurements in the memory 214.

The instructions 216 may include block 218, which includes instructionsfor adaptive thresholding. The processor 206 may determine a thresholdbased on a noise model 226. The noise model may be analytical,empirical, or a combination thereof. The noise model may receive, as aninput, a parameter of a measurement (e.g., a measured amount of lightreceived by the receiver) which may vary, for example, from onemeasurement to the next or from one measurement set to the next. As aresult, the noise model may generate an expected noise level (e.g., foreach measurement, or for any combination of measurements), and theprocessor may consequently determine a threshold, that may vary from onemeasurement to the next or from one measurement set to the next (e.g.,adaptive). If a given measurement is above the threshold, then themeasurement may be considered to be anomalously high and identified forconsideration as a true positive. The noise model 226 may include aspeckle noise model. The noise model 226 may generally include inputs(e.g., the measured amount of light received by the receiver) which maybe used to computationally describe the contributions of and/or behaviorof detector noise and/or speckle noise. The noise model 226 may be usedto adjust a value of the threshold based on the expected amount of noise(including speckle noise) for that measurement. The noise model 226 mayuse one or more parameters used to collect the measurements (e.g., scanrate, beam size/shape, etc.) to determine the expected amount of noise.In some embodiments, each of the measurements in a set of measurementsmay be compared to the same threshold value. In some embodiments, theremay be multiple different threshold values (e.g., a different thresholdfor one or more individual measurements in the set of measurements)applied to a set of measurements. The threshold value may be adaptivelydetermined at least in part for a given measurement (or group ofmeasurements) based on measurement parameters (e.g. the amount of lightmeasured by the transceiver) of that measurement or based on measurementparameters of a set of measurements. Measurements which are above theadaptive threshold may be identified for consideration as anomalous gasconcentration measurements. In some embodiments, measurements which arebelow the adaptive threshold for that measurement may be discarded orotherwise modified (e.g., weighted). In some embodiments, measurementswhich are above the threshold may be modified (e.g., weighted).

Since the adaptive threshold is based on a noise model, a statisticallevel of certainty that a given measurement is an anomalous measurementmay be calculated. For example, at least because a computational noisemodel is used in examples of adaptive thresholding described herein, alevel of certainly may be associated with the computation. Thus, for agiven adaptive threshold, the anomalous measurement may have some chanceof being a true positive (e.g., of representing an actual anomalous gasconcentration rather than noise). In some embodiments, the confidencefrom the adaptive filter may be stored in the memory 214 along with theconcentration associated with that measurement. In some embodiments, thelevel of confidence in the adaptive filter may be the same for each ofmeasurements. In some embodiments, the level of confidence in theadaptive filter may be different between one or more of themeasurements. In some embodiments, the level of confidence in theadaptive filter may be user selectable.

Instructions 216 also include block 220, which includes instructions forspeckle filtering. The speckle filter may be used to determine if anidentified anomalous measurement (e.g., identified based on the adaptivethreshold of block 218) is an outlier. The speckle noise model (andtherefore the overall noise model 226) may be based on certainassumptions about statistical properties of the speckle noise (e.g., adistribution of the speckle noise, a source of the speckle noise, etc.).The amount of speckle noise in each gas concentration measurement may bemeasured, and this may be used to identify certain measurements (e.g.,measurements which do not meet the assumptions of the model). Thesemeasurements may be identified, weighted, or rejected as contaminated byspeckle noise, and therefore likely to be outliers.

The speckle filtering may generate a measurement of the amount ofspeckle noise in a given measurement. The measured amount of specklenoise may be compared to the expected amount of noise from the noisemodel 226. If the measured amount of noise for a given measurementexceeds a threshold based on the expected amount of noise for thatmeasurement, the measurement may be considered an outlier. Outliermeasurements may be discarded or modified (e.g., weighted). Measurementswhich are not outliers may be retained. In some embodiments,measurements which are not outliers may be modified (e.g., byweighting).

The computing system 200 may also determine a confidence based on thespeckle filter. The outlier threshold may be based on the noise model226, and therefor may reflect a statistical probability. Thus,measurements which are retained by the speckle filter may have a certainconfidence or probability of representing an anomalous gas measurementrather than noise.

The instructions 216 also include block 222, which includes instructionsfor plume identification. The plume identification may further filteranomalous gas measurements (e.g., as determined by blocks 218 and/or220) and may increase a confidence that a given anomalous gasmeasurement represents a true positive. The plume identification may bebased on the idea that since the gas will tend to diffuse and/or beblown by wind away from a source of the gas, an anomalous gasconcentration measurement which represents an anomalous gasconcentration should have neighboring or nearby measurements which alsohave elevated concentrations of gas. The block 222 may includeinstructions for one or more techniques which may measure a spatialdistribution of gas measurements about a suspected source. The plumedetection may treat previously identified anomalous gas concentrationmeasurements (e.g., from blocks 218 and/or 220) as suspected sources. Insome embodiments, measurements may be determined to be part of a plumebased, at least in part, on the noise model 226. The plumeidentification may also include a plume filter, which may discard and/ormodify measurements which are not associated with a plume. The plumedetection may also determine a shape and/or direction of the gas plume.As with blocks 218 and 220, the plume identification of block 222 mayalso generate a confidence. The confidence may represent a probabilitythat a given plume represents a true positive.

In some embodiments, all three of blocks 218, 220, and 222 (e.g.,adaptive thresholding, speckle filtering, and plume identification) maybe used together to determine the location of anomalous gasconcentrations and their associated plumes. A set of measurements may beprovided by an optical system and/or may be retrieved from themeasurements 230 stored in the memory. The processor 206 may execute theinstructions in block 218 to apply an adaptive threshold to the set ofmeasurements and discard (and/or modify) measurements of the set ofmeasurements which fall below the adaptive threshold. This may provide afirst revised set of measurements. The processor 206 may then executethe instructions of block 220 to perform speckle filtering on the firstset of revised measurements. An amount of noise in the measurements maybe measured and used to determine if the measurement is an outlier.Those measurements which are outliers may be discarded (or modified).The measurements which are not determined to be outliers (e.g., themeasurements which are not discarded) may comprise a second set ofrevised measurements. The processor 206 may execute the instructions inblock 222 to perform plume identification on the second set of revisedmeasurements. Each of the remaining measurements in the second set ofrevised measurements may be investigated to determine if it has anassociated plume (additional information such as a direction of theplume may also be determined).

Each of the instructions associated with blocks 218-222 may also providea confidence that the measurements which are not discarded (or otherwisemodified) represent true positives. The computing system 200 maycalculate an overall confidence based on the adaptive thresholdconfidence, the speckle filter confidence, and the plume confidence. Ameasurement deemed as a true positive may be labeled with such aconfidence.

The instructions 216 may also include block 224, which may be executedby the processor 206 to generate spatial mapping. As described in moredetail in FIG. 3, block 224 may direct the processor 206 to generate amap of the spatial distribution of the anomalous measurements. In someembodiments, one or more maps may be generated based on the measurementset after being filtered by one or more of the instructions in box218-222. In some embodiments, the map may be generated with a spatialdistribution of the confidence that the measurement at each pointrepresents an anomalous gas concentration. In some embodiments, a mapshowing spatial distribution of gas concentration may be generated and aconfidence may be provided for one or more identified plumes. In someembodiments, block 224 may use additional information, such as locationinformation 228, which may represent a location at which each associatedmeasurement was made. The location information 228 may be provided by alocator 212, which may be a system capable of determining a locationover time of the measurements (e.g., a GPS). In some embodiments,measurement system may measure one or more spatial properties of thetarget area. For example, the measurement system may be able to measurea range to a surface in the target area. The collected range informationas the measurement system scans the target area may be used, forexample, to generate a topographical map of the target area.

The computing system 200 may also be coupled to be one or more externalcomponents, such as a display 202 and an input/output device (I/O) 204.In some embodiments, the display 202 may be used to display one or morepieces of information, such as a map of the concentration measurements(and/or a map of the confidence in those measurements). In someembodiments, the I/O 204 may allow a user to control one or moreoperations of the computing system 200. For example, the user may beable to select data in a specific area and apply one or more of thefilters in blocks 218-222 to it.

FIG. 3 is an example image of plume detection according to an embodimentof the present disclosure. The example image may represent examplemeasurements which may be collected and/or processed by the measurementsystem 100 of FIG. 1 and/or the computing system 200 of FIG. 2 in someembodiments. The image includes a map 302 representing a target area. Aregion 304 of the map 302 has been highlighted. The region 304 containsan identified gas plume 308 which is coming from a gas source 306. Eachof the boxes 310, 312, and 314 is a graphical representation of adifferent processing step being applied to measurements within theregion 304. As shown in the example of FIG. 3, the boxes 310-314represent successive filtering steps. The box 310 represents a data maskbased on an adaptive threshold, the box 312 represents a data mask basedon a speckle filter, and the box 314 represents a data mask based onplume identification. The image of the plume 308 on the map 302represents measurements which have been processed by each of thefiltering steps represented in the boxes 310-314.

The map 302 represents a target area (e.g., target area 116 of FIG. 1).In the example of FIG. 3, the target area is a wellsite, and the gaswhich is being measured is methane. The map 302 may represent an aerialview of the target area (e.g., as seen from the mobile platform 114 ofFIG. 1). The map 302 may be based on a pre-existing map of the targetarea and/or may be generated by the measurement system (e.g., by alocator 212 of FIG. 2). In some embodiments, the measurement system mayuse the lidar to measure a distance to a surface (e.g., the ground, atree, a structure, etc.) of the target area to generate the map 302. Insome embodiments, these distance or range measurements may be used todetermine elevations of the surfaces of the target area, and may be usedto generate a 3D dataset representing the topology of the target area.The example map 302 of FIG. 3 is a 2D representation of a 3D datasetthat mapped elevations of surfaces of the target area. In someembodiments, the map may include aerial photography. In someembodiments, the map may include satellite imagery. In some embodiments,the measurement system may map the target area at the same time that gasconcentration measurements are being collected. In some embodiments, thesame optical system may both map and measure gas concentrations in thetarget area.

The map 302 includes a region 304, which has been selected forillustrative purposes. The region 304 has been selected because itincludes a region of the map 302 which represents a portion of thetarget area which includes a gas source 306 emitting the gas plume 308.In the map 302, the gas plume 308 may be represented as a color map (orheat map). In the example of FIG. 3 brighter colors within the plumeindicate higher gas concentrations. The color map of the gasconcentration measurements (including the gas plume 308) may be overlaidon top of the map 302. Before the gas concentration measurements areoverlaid on the map 302, they may be filtered so that only anomalous gasconcentrations are overlaid on the map 302.

Each of the boxes 310-314 represents one of the filtering steps used togenerate the heat map of the gas plume 308 which is overlaid on the map302. In general, each of the boxes 310-314 shows a data mask which isapplied to the gas concentration measurements in the region 304. Eachpixel in the first two boxes 310 and 312 may represent an individualmeasurement point recorded within the region 304. Dark areas (e.g.,black pixels) in the boxes 310-312 represent measurements which areretained. White areas (e.g., white pixels) in the boxes 310-312represent measurements which are discarded. The white area of box 314also represents measurements which are discarded (as not part of theplume), while the shaded in region represents a plume. The shading inbox 314 represents concentration of gas in the plume with lighter shadesindicating higher concentration.

The box 310 represents a data mask associated with adaptivethresholding. Each of the measurements (e.g., each of the pixels in thebox 310) may be compared to an adaptive threshold calculated based on anexpected amount of noise associated with that measurement. The expectedamount of noise may be calculated based on a noise model, which mayinclude a speckle noise model. The noise may be expressed as anequivalent concentration measurement based on the noise. If themeasurement is greater than an adaptive threshold it may be retained(e.g., the pixel will be black), while if the measurement is below theadaptive threshold it may be discarded (e.g., the pixel may be white).

The box 312 represents a speckle filter which is applied to the dataafter the data mask in box 310 is applied. The speckle filter maymeasure an amount of speckle noise in each of the measurements that wereretained after box 310. The speckle filter may filter the measurementsbased on the amount of measured speckle noise. Similar to box 310, amask may be applied and measurements associated with the dark pixels maybe retained.

Box 314 represents plume detection. The plume detection may filter basedon groups of individual measurements. The plume detection may be appliedto the measurements which are retained in box 312. The plume detectionshown in box 314 represents both a source (at the bottom left of theshaded region) and a direction of the plume as the concentrationgradient decreases towards the upper right of the box 314. Theconcentration information and region of the plume in box 314 may becombined with the retained measurements in box 312 to achieve the heatmap of the plume 308 which is overlaid on the map 302.

FIG. 4 is a graph depicting a detection limit according to an embodimentof the present disclosure. The graph 400 may represent the behavior of anoise model which, in some embodiments, may be used for determining anadaptive threshold (e.g., may be used to implement block 218 of FIG. 2).The x-axis of the graph represents the optical power received by thereceiver (e.g., receiver 112 of FIG. 1) and is a log scale. The y-axisrepresents the detection limit (e.g., a lowest concentration of gaswhich is detectable over the noise) and is also a log scale. The line404 represents a detection limit determined solely by speckle noise.Since the detection limit from speckle noise is based on a physicalproperty of the light (its coherence), it may be relatively constantwith received optical power. The line 406 represents a detection limitdetermined solely by a detector (e.g., thermal noise in theelectronics). As the amount of received power decreases, thecontribution of the detector noise to the lower detection limit maybecome more significant.

The graph 400 may be based on a noise model which includes termsrepresenting the noise from the detector (e.g., line 406) and a specklenoise model (e.g., line 404). The noise from the detector may beexpressed as noise equivalent power (NEP). The speckle noise may berepresented by the speckle interference carrier-to-noise ratio(CNR_(speckle)). The noise model may provide an expression for thepath-integrated gas concentration noise (C_(noise)). The noise model maybe expressed by equation 1, below:

$\begin{matrix}{C_{noise} = {\frac{1}{2\;\gamma}\sqrt{\left( \frac{NEP}{P_{R}\sqrt{T_{m}}} \right)^{2} + \left( \frac{\rho}{{CNR}_{spckle}} \right)^{2}}}} & {{Eqn}.\mspace{14mu} 1}\end{matrix}$

In equation 1, γ is a coefficient that relates the lidar signal to theconcentration of the target gas species, P_(R) is the light powerreceived by the lidar system and ρ is a coefficient for the couplingstrength of speckle interference to the lidar measurement as a functionof the target range extent. The noise model may be expressed by equation2, below:

$\begin{matrix}{{CNR}_{speckle} = {\sqrt{N_{avg}} = {\sqrt{N_{TxRx}M_{scan}} = \sqrt{\left( {1 + \left( \frac{\sqrt{2\;\ln\; 2}D_{rec}\theta_{trans}}{\lambda} \right)^{2}} \right)\left( {1 + \frac{\omega_{scan}T_{m}}{\theta_{trans}}} \right)}}}} & {{Eqn}.\mspace{11mu} 2}\end{matrix}$

In equation 2, N_(avg) is the total number of speckle cells averaged permeasurement. N_(TxRx) represents the number of speckle cells averagedper measurement due to the geometry of the beam illuminating the remotetarget and the imaging properties of the lidar receiver. Here, DJ cisthe diameter of the lidar receiver, θ_(trans) is the half-angle Gaussiandivergence of the transmitted beam and A is the wavelength of thetransmitted beam. M_(scan) is a multiplicative factor for the number ofadditional speckle cells averaged per measurement due to spatialscanning of the lidar beam. In this term, ω_(scan) is the angular speedat which the lidar beam is scanned across the remote target and T_(m) isthe measurement duration.

The line 402 represents a la path-integrated detection confidence limitfor a WMS lidar system detecting methane at a wavelength of 1650 nm. The1σ confidence limit may represent a statistically expected amount ofnoise in the measurement based on C_(noise). The line 404 represents theterm of Equation 1 which includes the speckle noise model CNR_(speckle)while the line 406 represents the term including the detector noise NEPin this scenario.

Each measurement in a set of measurements may be compared to an adaptivethreshold may be based on the expected amount of noise. In someembodiments, the threshold may be based on a multiplicative factor n ofthe 1σ confidence limit. The 1σ confidence limit may represent astatistical variable which quantifies detection confidence. When athreshold is chosen that is a multiple n of the 1σ, there may aprobability (e.g., a confidence) p that a given measurement is a falsepositive. The confidence may be given by the Gauss error function erfaccording to equation 3, below:

$\begin{matrix}{p = {er{f\left( \frac{n}{\sqrt{2}} \right)}}} & {{Eqn}.\mspace{11mu} 3}\end{matrix}$

Equations 1-3 may be based on certain statistical assumptions about theproperties of the measurement signals and the noise. In particular,Equation 2 may assume a measurement scenario where the target surfacearea illuminated by the lidar beam has approximately uniformreflectivity and a random distribution of surface roughness within eachspeckle cell. These assumptions may be valid for a some measurementconditions. However, certain measurements within a set of measurements(e.g., all the measurements of a target area) may not conform to theseassumptions. For example, within a given measurement area, there may bea particular area of strong scattering (and/or reflection), and thusthere is not uniform reflectivity. This may lower the effective numberof speckle cells averaged in the measurement due to the non-uniformspatial distribution of received signals (e.g., more signals will comefrom the strong scattering region). In another example, there may be afirst semi-transparent (and/or partially blocking) first surface auniform distance in front of a second surface. The effective number ofspeckle cells averaged in this measurement scenario may be significantlyreduced due to presence of multiple reflective surfaces corresponding toeach speckle cell, and the high degree of uniformity in the separationbetween these surfaces across the illuminated areas and on each surface.

The presence of measurements in gas concentration lidar data setscorresponding to targets with a small number of speckle cells, or otherfactors, may lead to non-Gaussian behavior in the measurement statisticsand/or result in CNR measurements that do not follow or are not wellapproximated by Equation 2. Specifically, such data sets may containoutlier measurement noise events with frequency of occurrence thatexceeds the number expected according to Gaussian statistics and/orbased on Equation 2. Such outlier noise events may be misinterpreted asanomalous gas concentration measurements. Such non-Gaussian measurementstatistics may therefore lead to higher occurrence of false positives,lower confidence of detection events, and/or poorer sensitivity lidargas concentration measurements.

The outlier noise events may be identified by using a speckle filter(e.g., as in block 220 of FIG. 2) to measure the contribution of specklenoise to a given measurement. In an example system where WMS is used,the amount of measured speckle noise may be quantified based on analysisof harmonics of the frequency at which the emitted laser beam ismodulated. FIG. 5 is an example illustration of such a scenario.

FIG. 5 is a graph of in-phase and out-of-phase harmonics according to anembodiment of the present disclosure. Herein, “out-of-phase” may referto an orthogonal component relative to an “in-phase” component. Thegraph 500 represents an example of how harmonics of the amplitudemodulation may be used to measure an amount of speckle noise in a givenmeasurement. The graph 500 has two parts, graph 501 which shows in-phaseamplitude, and graph 502, which shows out-of-phase amplitude. The x-axisof both graphs 501 and 502 is the harmonic of the fundamental modulationwave. The y-axis is the amplitude in decibels (dB).

During WMS, the laser may be modulated with a certain frequency. As maybe seen from the received signals in the in-phase graph 501, the lasersignal contributes to peaks at each of the in-phase harmonics. Thesignal from the laser may diminish with each successive harmonic, andmay become negligible after a certain harmonic. The laser may have no(or minimal) contribution to peaks in the out-of-phase graph 502.

The absorption of the gas may contribute to peaks in both the in-phasegraph 501 and out-of-phase graph at even harmonics. The contribution ofthe gas absorption may be used to determine the concentration of thegas. The signal from the gas may also diminish with each successive peak(e.g., with each odd harmonic), and may become negligible at a certainpoint.

Speckle interference due to the coherence of the laser light maycontribute to peaks at each of the harmonics in both the in-phase graph501 and the out-of-phase graph 502. Like the other signals, the signalsdue to speckle interference may decrease with each harmonic and maybecome negligible after a certain point. For the out-of-phase graph 502,the odd harmonic peaks 504 may be entirely (or primarily) based on thespeckle noise. Thus, the out-of-phase odd harmonic peaks 504 may be usedto measure an amount of speckle noise in a given measurement.

The ratio of the in-phase first and second harmonic amplitudes may berelated to the gas concentration by equation 4, below:

$\begin{matrix}{C = \frac{m\; A_{2\; f}}{2\;\gamma\; A_{1\; f}}} & {{Eqn}.\mspace{11mu} 4}\end{matrix}$where A_(2f) and A_(1f) are the in-phase first and second harmonicamplitudes, m is the laser intensity modulation depth and γ is acoefficient that relates the harmonic amplitude ratio to gasconcentration. The contribution of speckle noise may distort the gasconcentration measurement in the second harmonic. The distortion to thesecond harmonic p_(2f) may be modeled as a combination of the distortionto the first harmonic p_(1f) and the distortion to the third harmonicp_(3f) along with a pair of best fit coefficients a and b, as shown inequation 5 below:ρ_(2f) =aρ _(1f) +bρ _(3f)  Eqn. 5

The best fit coefficients derived from the distortion of the in-phaseharmonics may be used with out-of-phase harmonics to estimate ameasurement of speckle interference contribution to the gasconcentration Csi. This may involve the out-of-phase first and thirdamplitudes Aout_(1f) and Aout_(3f), respectively. The measurement ofspeckle interference noise Csi may be given by combining equations 4 and5 to yield equation 6, below:

$\begin{matrix}{C_{si} = \frac{m\left( {{\alpha\; A\;{out}_{1f}} + {{bA}\;{out}_{3\; f}}} \right)}{2\;\gamma\; A_{1\; f}}} & {{Eqn}.\mspace{11mu} 6}\end{matrix}$

The speckle contribution Csi may be used to process the measurements.For example Csi could be compared to the expected noise Cnoise given byequation 1. In particular, a filter could be used which is based on theoutcome of the comparison C_(si)≥MC_(noise), where M is a multiplicativefactor applied to the expected measurement noise level C_(noise). IfC_(si) exceeds MC_(noise) the measurement may be identified ascontaining an excessive contribution from speckle interference and maybe excluded from the data set, given a modified confidence rating orscrutinized using additional information or metrics. Other methodologiesfor determining a relative contribution of speckle noise may also beused.

In this manner, the measured amount of speckle noise in a measurementmay be used to determine if the measurement may be an outlier. Themeasured amount of noise may be compared to a multiple of the expectedamount of noise, and the measurement may be rejected (or weighted orotherwise modified) if the measured amount of noise exceeds the athreshold, which may be based on a multiplicative factor times theexpected amount of noise. In this manner, measurements which areoutliers (e.g., because they violate assumptions of the noise model) maybe filtered out of the data set.

FIG. 6 is a schematic diagram depicting gas plume detection according toan embodiment of the present disclosure. The gas plume detectionrepresented in FIG. 6 may illustrate principals which may be used toimplement the gas plume identification 222 of FIG. 2, in someembodiments. The gas plume 602 is represented on an x-y axis whichrepresent the cardinal map directions, and by a color scale 604 whichrepresents a concentration of the gas at a given point in space. The gasplume 602 is emitted from a source 610, and is caused by a winddirection 606 to elongate in the ‘downwind’ direction (in this example,due east).

One example method of plume detection may involve computations todetermine the quantity of gas near a suspected emission source 610 as afunction of direction from the suspected emission source 610. The plumedirection may use a given measurement as a suspected source of theplume. The measurement used as the suspected source may be one of theanomalous gas concentration measurements identified by adaptivethresholding and/or speckle filtering. In some embodiments, differentpotential sources of the plume may be investigated in an iterativemanner. Computations for determining the gas quantity versus directionfrom the suspected emission point may generally involve measuring aconcentration corresponding to a particular direction (e.g., due East asshown in the graph 600). The direction of measurement may then berotated to a new direction (e.g., along direction 608). By performingsuch concentration measurements in multiple directions, the direction ofhighest concentration may be determined and may correspond to a plumedirection.

In one embodiment, and example method of calculating concentrationcorresponding to a direction may involve taking particular lineintegrals along numerous integration lines 614 (indicated by L₁ throughL_(n)) at different distances from the suspected emission source 610, orcomputing an average gas concentration within an area 612 relative tothe suspected emission source 610.

If line integrals are used there may be many possible definitions forcomputing a gas concentration line integral CI_(n) along the n^(th)integration line 614. In one example, the line integral may be given byequation 7, below:CI _(n)=∫_(−L) _(n) ^(L) ^(n) Cdl≈Σ _(n=1) ^(N) C _(n) Δl  Eqn. 7where C represents the gas concentration map, C_(n) is the set ofconcentration measurements along the integration line and Δl is theseparation between the gas concentration measurements along theintegration line. The gas concentration line integrals or average gasconcentration computations may be performed corresponding to additionalradial directions at different angles, one of which is represented by608, relative to the source 610. The results of the gas concentrationcomputations (e.g., with equation 7) corresponding to multipledirections may be combined to produce a graph representing the gasconcentration as a function of direction. The angle corresponding to thehighest gas concentration may indicate the direction of the plume.

The integration lines 614 or area shape 612 may be orientedperpendicular to a line extending radially from suspected emissionsource 610, and the length of the lines 614 or width of the shape 612may depend on the radial distance from source 610. In other exampleembodiments, the integration lines may be oriented at angles other thanperpendicular. Although the integration lines 614 are shown as straightin the example of FIG. 6, in other example embodiments the lines 614 donot need to be straight and may have curvature.

Another possible method for plume detection may be to evaluate thenumber of gas concentration measurement pixels within an area 612 thatexceed a multiple of the expected noise level, C_(noise) (e.g., asprovided by Equation 1). For example, it may be sufficient to computethe number of measurements in a possible plume area that exceed somemultiple of Cnoise (e.g., measurements which exceed 2C_(noise)). Whilethe area 612 is shown a certain shape in FIG. 6, the area may be othershapes or sizes in other example embodiments.

The line integrals, the area concentration measurements, and/or othermethods of calculating a concentration may be carried out for multipleangular directions (e.g., for multiple values of θn). Relative to theanomalous gas concentration location it may be possible to construct aconcentration versus direction curve in order to determine an angulardependence of the concentration about the anomalous gas concentrationlocation. Also, these techniques may be used to simply determine thepresence of a plume, without necessarily determining the direction ofthe plume (or vice versa). For instance, even though each point of thedispersed tail of the plume within the shape 612 may be below a gasconcentration measurement threshold, an average over the shape mayenable a lower gas concentration measurement threshold and may therebyenable plume detection. Also, a thresholding step may be performed onmeasurements within an area 612 with a reduced threshold to furtheruncover possible measurements that are part of a plume.

Gas plume detection may be used as an additional filter to themeasurements collected by a measurement system (e.g., measurement system100 of FIG. 1). Since the gas concentration measurements may representmeasurements of actual gas in an environment, it may be expected thatthe gas may diffuse outwards from a source. Because of this it may beexpected that a region of anomalous (e.g., high) concentration would beassociated with a plume. The gas plume detection may determine if agiven high gas concentration (e.g., a suspected source 610) which hasbeen identified as an anomalous gas concentration (e.g., by adaptivefilter 218 and/or speckle filter 220 of FIG. 2) is associated with a gasplume. Anomalous gas concentrations which are not associated with a gasplume may be rejected as likely false positives. This rejection may bebecause it is physically unlikely to find a gas plume comprised of asingle elevated measurement point in space.

The plume filter may be based on a calculation or plot of theconcentration vs. a direction from the source. This plot may benormalized. A plume filter threshold relationship may be based onequation 7, below:

$\begin{matrix}{{CI}_{conc} \geq {n \times \frac{{CI}_{noise}}{\sqrt{N}}}} & {{Eqn}.\mspace{11mu} 7}\end{matrix}$

Here, CI_(conc) and CI_(noise) are sums of line integrals forintegration lines 614 (e.g., integration lines L₁ thru L_(n)), for thegas concentration and the expected gas concentration noise (e.g. fromEquation 1 or from a region suspected to not have anomalous gasconcentration), respectively, and N is the number of gas concentrationmeasurements integrated over for each CI_(conc) value. If CIconc isgreater than or equal to the plume threshold value set by the right sideof equation 7, then the anomalous gas concentration measurement used asa suspected source for calculating CIconc and CInoise, may be determinedto be associated with a plume. In other words, if CIconc is greater orequal to the plume threshold, then the associated anomalous gasconcentration measurement may be judged to be a true positive.

FIGS. 7A-7B are graphs depicting using plume detection as a filteraccording to an embodiment of the present disclosure. FIG. 7A representsa scenario where an anomalous gas concentration measurement (e.g., asidentified by adaptive thresholding 218 of FIG. 2) is associated with aplume, and is retained by the plume threshold. FIG. 7B represents ascenario where the anomalous gas concentration measurement is notassociated with a plume and is rejected by the plume threshold. Each ofFIGS. 7A and 7B includes a respective plume image in the absence ofnoise 702 a and 702 b, a respective plume image with noise 704 a and 704b, a respective adaptive threshold image 706 a and 706 b, and arespective plume filter image 708 a and 708 b. Each of the respectiveelements 702-708 may generally be similar between FIGS. 7A and 7B.

Plume images 702 a-b both show a direction on the x-y axis and measuredgas concentration represented as a brightness of the pixels. The plumeimage 702 b represents a single anomalous measurement with no associatedgas plume. The plume image 702 a represents the same anomalousmeasurement as in image 702 b, except in the image 702 a, the anomalousmeasurement is associated with a plume extending towards the right ofthe image 702 a. The images 702 a-b represent an idealized measurementwithout noise. Images 704 a-b each show the same data as 702 a-brespectively, except that in the images 704 a-b, a model of noise hasapplied to the data. As may be seen, it may be difficult to visuallyidentify the plume associated with the anomalous concentration in image704 a even though it is there.

Images 706 a-b show an adaptive threshold (e.g., as in block 218 of FIG.2) applied to the noisy data represented in the respective images 704a-b. The x-axis of images 706 a-b represent a number of the measurementwhile y-axis represents a calculated gas concentration at thatmeasurement number. The dashed line shows the adaptive threshold levelwhich was determined based on the expected noise level for this set ofmeasurements. In this example, the adaptive threshold has been set at a5σ level. As may be seen in both images 706 a-b, only the measurementassociated with the source of the gas plume has a concentration which isgreater than the adaptive threshold. Thus, in both images 704 a and 704b, only a single measurement point may be identified by the adaptivethreshold as being an anomalous gas concentration measurement. However,only the measurement associated with a plume in image 704 a mayrepresent an actual anomalous gas concentration.

Images 708 a-b both represent a graph produced by a plume detection(e.g., plume identification 222 of FIG. 2). In both images 708 a-b thex-axis is a rotational direction about a suspected origin, which in thiscase is the anomalous gas concentration measurement identified by theadaptive threshold of image 706 a-b. The y-axis represents a normalizedflux along that particular direction.

In the example of FIGS. 7A-7B, a 4σ threshold for plume detection may becomputed (e.g., with Equation 7) based on line integrals of the adaptivethresholds, C_(noise), corresponding to the integration paths used forthe gas concentration line integrals. The threshold may be used as a wayto determine the angular dependence of the gas concentration about asuspected source location. This example may illustrate how a plume thatmay not be visible in the gas concentration image 704 a may still bedetectable, and its direction may be determined, with high confidence.It may also be possible to use a weighted sum to perform theconcentration integrals for plume detection and to determine the plumedetection threshold. The gas concentration noise estimate, C_(noise),for each measurement, or another similar metric, may be used as theweighting factors for such sums.

FIG. 8 is a graph depicting multiple filters applied to measurement dataaccording to an embodiment of the present disclosure. The graph 800 mayrepresent how a combination of processing may be used to filter a largemeasurement set down to those points most likely to represent truepositive anomalous gas concentrations. The x-axis of the graph 800represents a measurement number assigned to each of the measurements.The y-axis represents the concentration calculated from thatmeasurement.

The black line 802 represents the individual measurements. Measurementswhich have a small black dot are ones which have been identified asbeing greater than an adaptive threshold. Note that some measurementswith small black dots have lower values than some measurements withoutblack dots. This may be due to the adaptive nature of the thresholdingand may not be the case for a constant thresholding method. Of themeasurements which have been identified as greater than the adaptivethreshold, some have been ‘flagged’ by the speckle filter (e.g., as inblock 220 of FIG. 2). These measurements are surrounded by a circle, asrepresented by measurement 806. The measurements which are flagged asoutliers by the speckle filter may no longer be considered as candidatesfor representing a true positive anomalous gas concentration. A plumefilter has also been applied to the measurements. The plume filter maybe applied to the measurements which are above the adaptive threshold,but have not been flagged with a speckle filter (e.g., marked with a dotbut not a circle). Points which are not associated with a plume may be‘flagged’ as not representing true anomalous gas concentrationmeasurements. These may be represented by points, such as measurement804, which are surrounded by a box.

The points which exceed the adaptive threshold, and not flagged byeither the speckle filter or the plume filter, may be considered to betrue positives. Since these measurements represent measurements whichwere not flagged by the plume filter, they may be associated with aplume. The dotted line area 808 shows a group of measurements which havebeen determined to represent a true positive anomalous gasconcentration.

Each of the previously discussed processing steps may be associated witha confidence level which may represent a likelihood that a falsedetection event may be present in a given lidar data set after theprocessing. Each of the different processing steps (e.g., adaptivethresholding, speckle filtering, and plume detection) may have arespective probability that a false positive may occur. The falsepositives may represent measurements which are judged to representanomalous gas concentrations by the filter, even though they do not.

The probability (p_(false-at)) of a concentration measurement exceedingthe adaptive threshold (e.g., as in block 218 of FIG. 2) may be based onequation 8, below:

$\begin{matrix}{p_{{false} - {at}} = {\left( {p_{Gauss} + p_{outlier}} \right) = \left\lbrack {1 - {{erf}\left( \frac{n_{sig}}{\sqrt{2}} \right)} + p_{at}} \right\rbrack}} & {{Eqn}.\mspace{11mu} 8}\end{matrix}$where p_(Gauss) is the probability of a measurement exceeding theadaptive threshold due to random noise and pa, is the probability of ameasurement following non-Gaussian statistics exceeding the adaptivethreshold. The value of the adaptive threshold may be set at a value ofn_(sig)×C_(noise), where C_(noise) is based on equation 1 and n_(sig) isa multiple applied to set the level of the threshold.

The probability (p_(false-at)) may be used to compute the expectednumber of false detection measurements N_(false-at) using equation 9,below:N _(false-at) =p _(false-at) ×N _(meas)  Eqn. 9

where Nmeas is the number of measurements in given set of measurements.

The probability of observing a false detection measurement(p_(false-si)) after application of the speckle filter step (e.g., as inblock 220 of FIG. 2) may be based on equation 10, below:p _(false-si) =p _(Gauss) +p _(outlier) p _(si)  Eqn. 10

where p_(si) is the probability of an outlier measurement not beingidentified by the speckle filter.

The probability (p_(false-si)) may be used to compute the expectednumber of false detection events using equation 11, below:N _(false-si) =p _(false-si) ×N _(meas)  Eqn. 11

The probability of observing a false plume detection (p_(false-plume))after application of the speckle interference filter and the plumedetection filter may be determined based on equation 12, below:

$\begin{matrix}{p_{{false} - {plume}} = {{\left( {p_{Gauss} + {p_{outlier}p_{si}}} \right)\left( {1 - {{erf}\left( \frac{n_{{sig} - {plume}}}{\sqrt{2}} \right)}} \right)} + p_{plume}}} & {{Eqn}.\mspace{11mu} 12}\end{matrix}$where n_(sig-plume) is the random noise threshold for plume detectionfilter (e.g., n in Equation 7) and p_(plume) is the probability of afalse plume detection event not being identified by the plume detectionfilter.

The probability of observing a false positive such as a false detectionmeasurement (or plume) in a lidar gas concentration data set before andafter each filtration step may be determined based on the previouslycomputed probabilities. The probability of observing at least one falsedetection (p_(jd)) in a data set may be computed using equation 13,below:

$\begin{matrix}{p_{fd} = {\begin{pmatrix}n \\k\end{pmatrix}{p^{k}\left( {1 - p} \right)}^{n}}} & {{Eqn}.\mspace{11mu} 13}\end{matrix}$where p is the probability of observing a false detection measurement(or plume) from Equations 8, 10, or 12, n is the number of measurementsin the data set and k=0. Other measurement expectation parameters mayalso be computed using similar statistical analysis, such as detectionconfidence of anomalous gas concentration.

Estimation of the detection confidence for individual gas concentrationmeasurements may be complicated by the presence of outlier measurementsthat may assume values covering much of the gas concentrationmeasurement range. However, plume detection may be much less sensitiveto the presence of outlier measurements, and therefore may permitcomputation of reliable detection confidence estimates. The confidencethat may be assigned to a plume detection (p_(det)) in a data set may becomputed using equation 14, below:

$\begin{matrix}{p_{\det} = {\begin{pmatrix}n \\k\end{pmatrix}{p_{{false} - {plume}}^{k}\left( {1 - p_{{false} - {plume}}} \right)}^{n}}} & {{Eqn}.\mspace{11mu} 14}\end{matrix}$where the computation of (p_(false-plume)) may be performed using avalue for n_(sig-plume) determined by the plume detection peak heightand the plume detection noise.

In an example calculation, the plume detection shown in image 708 a ofFIG. 7A would result in n_(sig-plume)=5 because the peak height is 1σabove the 4σ adaptive threshold. If this plume was detected in the dataset shown in FIG. 8, application of equation 14 would result in ananomalous gas concentration detection confidence for this plume of99.99%. In this case, the seemingly high detection confidence in such asmall plume, relative to the measurement noise, may rely on theassumption that p_(plume)<1E-9. The confidence in any given plumedetection may ultimately be limited by the value of p_(plume), which maybe determined empirically and may be a function of other plumeattributes such as size or other contextual information. To furtherreduce the sensitivity of plume detection to outlier measurements it maybe desirable to remove one or more outliers from the area where theplume detection algorithm will be applied. Removal of the pointidentified as the emission source may guard against a false positiveplume detection in the event that that point is an outlier measurement.

For brevity, the operation of the optical systems herein have generallybeen described with respect to light being emitted by the optical systemtowards a target area. However, one of skill in the art would appreciatethat since optical paths may typically be reversible, the beam path mayalso represent a field of view ‘seen’ by the optical system (e.g., reacha receiver of the optical system).

Certain materials have been described herein based on their interactionwith light (e.g., opaque, reflective, transmissive, etc.). Thesedescriptors may refer to that material's interactions with a range ofwavelength(s) emitted by the system and/or that the receiver issensitive to. It would be understood by one of skill in the art that agiven material's properties vary at different ranges of wavelengths andthat different materials may be desired for different expected ranges ofwavelength(s). The description of a particular example material is notintended to limit the disclosure to a range of wavelengths over whichthat particular example material has the desired optical properties. Theterm ‘light’ may be used throughout the spectrum to representelectromagnetic radiation, and is not intended to limit the disclosureto electromagnetic radiation within the visible spectrum. The term‘light’ may refer to electromagnetic radiation of any wavelength.

Of course, it is to be appreciated that any one of the examples,embodiments or processes described herein may be combined with one ormore other examples, embodiments and/or processes or be separated and/orperformed amongst separate devices or device portions in accordance withthe present systems, devices and methods.

Finally, the above-discussion is intended to be merely illustrative ofthe present system and should not be construed as limiting the appendedclaims to any particular embodiment or group of embodiments. Thus, whilethe present system has been described in particular detail withreference to exemplary embodiments, it should also be appreciated thatnumerous modifications and alternative embodiments may be devised bythose having ordinary skill in the art without departing from thebroader and intended spirit and scope of the present system as set forthin the claims that follow. Accordingly, the specification and drawingsare to be regarded in an illustrative manner and are not intended tolimit the scope of the appended claims.

What is claimed is:
 1. A method comprising: obtaining, using a lightdetection and ranging (LIDAR) system, a set of gas concentrationmeasurements from a target area; discarding or modifying certainmeasurements of the set of gas concentration measurements based on acomparison of measurements in the set of gas concentration measurementsto an adaptive threshold with a value based on an expected noise level,wherein the value of the adaptive threshold varies depending onparameters of the measurement, to provide a revised set of measurements;and determining a presence of an anomalous gas concentration based onthe revised set of measurements.
 2. The method of claim 1, furthercomprising determining a confidence that a remainder of the set of gasconcentration measurements after discarding or modifying the certainmeasurements represent anomalous gas concentrations.
 3. The method ofclaim 1, wherein the expected noise level is based on a noise modelcomprising a model of speckle noise in the set of gas concentrationmeasurements.
 4. The method of claim 3 wherein the noise model furthercomprises a detector noise model.
 5. The method of claim 1, wherein thevalue of the adaptive threshold is a multiple of the expected noiselevel.
 6. The method of claim 1, wherein the value of the adaptivethreshold is used to determine a confidence that gas concentrationmeasurements which are above the value represent true positives.
 7. Themethod of claim 1, wherein the value of the adaptive threshold is based,at least in part, on an amount of light received by the LIDAR system. 8.A method comprising: obtaining, using a light detection and ranging(LIDAR) system including a laser source modulated at a modulationfrequency, a set of gas concentration measurements from a target area;discarding or modifying certain measurements of the set of gasconcentration measurements based at least in part on a signal amplitudepresent in at least one odd harmonic of the modulation frequency toprovide a revised set of measurements; and determining a presence of ananomalous gas concentration based on the revised set of measurements. 9.The method of claim 8, further comprising measuring an amount of specklenoise in the measurement based on the signal amplitude.
 10. The methodof claim 9, further comprising determining an expected amount of specklenoise based on a speckle noise model, and comparing the measured amountof speckle noise to the expected amount of speckle noise.
 11. The methodof claim 8, further comprising determining a confidence that a remainderof the set of gas concentration measurements after discarding ormodifying the certain measurements represent anomalous gasconcentrations.
 12. A method comprising: obtaining, using a lightdetection and ranging (LIDAR) system, a set of gas concentrationmeasurements from a target area; determining, based on a speckle noisemodel, at least one anomalous gas concentration measurement in the setof gas concentration measurements; and determining a presence of a gasplume associated with the at least one anomalous gas concentrationmeasurement and one or more of the set of gas concentration measurementsnearby a location of the at least one anomalous gas concentrationmeasurement.
 13. The method of claim 12, further comprising determininga direction, location and/or source of the gas plume.
 14. The method ofclaim 12, further comprising determining if each of the at least oneanomalous gas concentration measurements is associated with a gas plume,and modifying or discarding certain of the at least one anomalous gasconcentration measurements which are not associated with a gas plume.15. The method of claim 12, wherein determining the presence of the gasplume comprises integrating along a plurality of lines which areperpendicular to the direction of the gas plume.
 16. An apparatuscomprising: an optical system comprising a laser source configured tomodulate at a modulation frequency, the optical system configured torecord a set of gas concentration measurements based on received lightfrom a target area; at least one processor; and a memory, the memoryencoded with executable instructions, which, when executed by the atleast one processor cause the apparatus to: discard or modify certainmeasurements of the set of gas concentration measurements based on acomparison of measurements in the set of gas concentration measurementsto an adaptive threshold with a value based on an expected noise levelto provide a first revised set of measurements; identify certain of themeasurements of the first revised set of measurements as outliers anddiscard or modify the outliers to provide a second revised set ofmeasurements; determine a presence of a gas plume based on at least onemeasurement point in the second revised set of measurements and discardor modify measurements of the second revised set of measurements whichare not associated with the gas plume.
 17. The apparatus of claim 16,wherein the executable instructions further comprise instructions tocause the apparatus to determine a detection confidence.
 18. Theapparatus of claim 17, wherein the executable instructions furthercomprise instructions to cause the apparatus to determine a firstconfidence based on the first revised set of measurements, a secondconfidence based on the second revised set of measurements, and a thirdconfidence based on the gas plume, and wherein the processor isconfigured to determine the detection confidence based on the first,second, and third confidences.
 19. The apparatus of claim 17, whereinthe executable instructions further comprise instructions to cause theapparatus to generate a map based on the detection confidence.
 20. Theapparatus of claim 16, further comprising a mobile platform configuredto support the optical system and move relative to the target area. 21.The apparatus of claim 16, wherein the optical system is configured todetermine range information between the optical system and surfaces ofthe target area, and wherein the map is based on the gas plume and therange information.
 22. The apparatus of claim 16, wherein the executableinstructions further comprise instructions to cause the apparatus todetermine the expected noise level based on a noise model comprising aspeckle noise model.
 23. The apparatus of claim 16, wherein theexecutable instructions further comprise instructions to cause theapparatus to identify the outliers based, at least in part, on a signalamplitude present in at least one odd harmonic of the modulationfrequency.
 24. The apparatus of claim 16, wherein the executableinstructions further comprise instructions to cause the apparatus todetermine the presence of the gas plume based on an angular dependenceof the concentration about the at least one measurement point in thesecond set of revised measurements.